Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Use of Laser-Induced Bubbles in Intraocular Pressure Measurement: A Preliminary Study(IOP Publishing Ltd, 2018-11-23) Altindis, Fatih; Ozdur, Ibrahim T.; Mutlu, Sait N.; Yilmaz, BulentThis work investigates the feasibility of a novel approach for measuring intraocular pressure (IOP) by analyzing micron-level laser-induced bubble characteristics in the intraocular fluid. We believe that this concept may be used as a non-invasive alternative for measuring a patient's IOP by analyzing the laser-induced bubble volume in the intraocular fluid in the anterior chamber of the eye. The behavior of laser-induced bubbles was examined under differing fluid pressure levels and at differing laser pulse energy levels. An intraocular medium-like environment was imitated and an imaging system was designed in order to capture laser-induced bubbles with their movements. The video recordings of the bubbles were processed using custom software, and the volume of the bubbles was estimated using three different approaches. The bubble volumes were estimated more accurately by using the rising velocity of the bubble rather than its direct radii appearances on the images. An inversely proportional relationship was observed between the laser-induced bubble volume and the fluid pressure. IOP can be measured with a non-invasive technique using laser-induced bubble volume. Deeper and detailed studies, including clinical studies, may lead to the use of lasers for measuring IOP.Conference Object Citation - WoS: 7Citation - Scopus: 12Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces(European Signal Processing Conference, EUSIPCO, 2018-09) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 5Citation - Scopus: 6Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors(IEEE-Inst Electrical Electronics Engineers Inc, 2023-10) Altindis, Fatih; Banerjee, Antara; Phlypo, Ronald; Yilmaz, Bulent; Congedo, MarcoThis article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12 +/- 1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.Article Citation - WoS: 56Citation - Scopus: 69Synchrosqueezing Transform Based Feature Extraction From EEG Signals for Emotional State Prediction(Elsevier Sci Ltd, 2019-07) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentThis paper presents a novel method for emotion recognition based on time-frequency analysis using multivariate synchrosqueezing transform (MSST) of multichannel electroencephalography (EEG) signals. With the advancements of the multichannel sensor applications, the need for multivariate algorithms has become obvious for extracting features that stem from multichannel dependency in addition to mono-channel features. In order to model the joint oscillatory structure of these multichannel signals, MSST has recently been proposed. It uses the concepts of joint instantaneous frequency and bandwidth. Electrophysiological data processing mostly requires joint time-frequency analysis in addition to both time and frequency analysis separately. The short-time Fourier transform (STFT) and wavelet transform (WT) are the main approaches utilized in time-frequency analysis. In this paper, the feasibility and performance of multivariate wavelet-based synchrosqueezing algorithm was demonstrated on EEG signals obtained from publically available DEAP database by comparing with its univariate version. Eight emotional states were considered by combining arousal-valence and dominance dimensions. Using linear support vector machines (SVM) as a classifier, MSST and its univariate version resulted in the highest prediction accuracy rates of (9) over tilde3% among all emotional states. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 27Citation - Scopus: 30Super Resolution Convolutional Neural Network Based Pre-Processing for Automatic Polyp Detection in Colonoscopy Images(Pergamon-Elsevier Science Ltd, 2021-03) Tas, Merve; Yilmaz, BulentColonoscopy is the most common methodology used to detect polyps on the colon surface. Increasing the image resolution has the potential to improve the automatic colonoscopy based diagnosis and polyp detection and localization. In this study, we proposed a pre-processing approach that uses convolutional neural network based super resolution method (SRCNN) to increase the resolution of the training colonoscopy images before the localization of polyps. We also investigated the use of CNN based models such as the Single Shot MultiBox Detector (SSD) and Faster Regional CNN (RCNN) for real-time polyp detection and localization. Our results showed that using SRCNN method before the training process provides better results in terms of accuracy in both models compared to the low-resolution cases. Furthermore, we reached an F2 score of 0.945 for the correct localization of colon polyps using Faster RCNN with ResNet-101 feature extractor.Article Citation - WoS: 7Citation - Scopus: 9Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis(Springer, 2022-02-02) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThis study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.Conference Object Citation - Scopus: 2Sliding Window and Filterbank Utilization on Riemannian Geometry(Institute of Electrical and Electronics Engineers Inc., 2022-08-08) Altindis, Fatih; Yilmaz, BulentRiemannian geometry-based signal processing approaches on EEG signals provides similar decoding performance compared to state-of-the-art methods. However, Riemannian geometry framework requires predefine EEG signal epoch that is to be used in the analysis. Sliding window approach that operates in Riemannian geometry proposed to enable use of EEG signals without constrained by the record length. Decoding performance of tangent space mapping was increased more than 6% in overall accuracy compared the previous study's results. Instead of using single band-pass filter, utilization of filterbank is proposed to increase decoding performance. Distance based Riemannian classifier's overall performance were increased by 5% compared to standard Riemannian geometry approach. © 2022 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Robust Controller Electromyogram Prosthetic Hand With Artificial Neural Network Control and Position(Indian Journal of Forensic Medicine and Toxicology ijfmt@hotmail.com, 2020) Ahmed, Saygin Siddiq; Ahmed, Aydin S.; Yilmaz, Bulent; Doǧru, NuranIn this study, we proposed and designed a new control method for an electromyographically (EMG) controlled prosthetic hand. The objective is to increase the control efficiency of the human–machine interface and afford greater control of the prosthetic hand. The process works as follows: EMG biomedical signals acquired from Myoware sensors positioned on the relevant muscles are sent to the robot that consist of hand, Arduino and MATLAB program, which computes and controls the hand position in free space along with hand grasping operations. The Myoware device acquires muscle signals and sends them to the Arduino. The Arduino analyzes the received signals, based on which it controls the motor movement. In this design, the muscle signals are read and saved in a MATLAB system file. After program processing on the industrial hand which is applied by MATLAB simulation, the corresponding movement is transferred to the hand, enabling movements, such as, hand opening and closing according to the signal stored in the MATLAB system. In this study, hand and fingerprints were designed using a three-dimensional printer by separate recording finger and thumb signals. The muscle signals were then analyzed in order to obtain peak signal points and convert them into data. These results indicate the effectiveness of the proposed method and demonstrate the superiority of the method for amputees because of the improved controllability and perceptibility afforded by the design. © 2020 Elsevier B.V., All rights reserved.Article Citation - WoS: 14Citation - Scopus: 15Relationship Between Objective and Subjective Cognitive Load Measurements in Multimedia Learning(Routledge Journals, Taylor & Francis Ltd, 2020-11-15) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThe aim of this study is to compare subjective and objective cognitive load measurements in a multimedia learning environment. For this purpose, 20 university students studied in multimedia environments designed by researchers during which eye movements and multichannel electroencephalography (EEG) signals were recorded. Self-report ratings were obtained at the end of the experiment, and retention performances of the students were measured. After the data were collected, Pearson Correlation analysis was applied. According to the results, significant relationship between the number of fixations and EEG frequency band powers was found. In addition, there was a negative relationship between retention performance and number of fixations. Moreover, a negative relationship was found between retention performance and self-reported measurements.Article Citation - WoS: 1Citation - Scopus: 1Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women(Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, FatihNeuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.